What does "class-level discriminative feature representation" mean in the paper "Semi-Supervised Deep Learning with Memory"?
Discriminative models give the probability of an element in the feature space $x \in X$ belonging to a class $y \in Y$, i.e. $p(Y|X)$, where $Y$ is the set of classes in a classification problem.
The discriminative feature representation in this context means the feature map/s  which are the output/s of the convolutional layers of the backbone (the convolutional layers of the neural network) which (presumably) differ on the basis of their class, and can be used to discriminate which class the original image belongs to, in the case of image classification.